Non-Technical Summary of"Bidding Behavior in the Price is Right Game: An Experimental Study"
by Paul J. Healy & Charles Noussair

In the Price is Right game show, players compete in "Contestants' Row" to win a prize and the right to play
additional games on the show. In Contestants' Row, four bidders sequentially announce guesses ("bids") of the prize.
The logic of game theory (due in large part to the work of Nobel laureates John Nash - subject of the movie "A Beautiful Mind" - and Reinhard Selten) makes certain
clear predictions about how people should behave in this game. For example, if the fourth bidder wants to maximize her chances of winning,
she should either "cut off" one of the previous three bidders (outbidding them by exactly one dollar) or bid one dollar. In a previous study
(Berk et al. (1996)) it was shown that fourth bidders on the TV show employ one of these strategies only 57
percent of the time.

Why don't fourth bidders always play optimally? Do they fear that cutting off another player will induce that player
to reciprocate in a later rounds of Contestants' Row? Are they worried about how their behavior will be perceived
by the studio and television audiences? Or, are they simply unable to calculate the optimal strategy in this
situation? We also observe sub-optimal play by earlier bidders; is this because they have the difficult task of
predicting future bidders' behavior when choosing their own optimal choices? By playing the game in the laboratory
with careful manipulations of the details of the game, we can explore the exact causes of sub-optimal play.

First, our laboratory subjects play the game 50 times. If subjects have difficulty calculating the optimal
strategy in early periods, we should see better performance in later periods as subjects learn better strategies.
Second, we compare a setting where subjects play face-to-face and know the identities of their competitors to a
setting of anonymity where subjects play by computer and cannot track competitors' identities. If subjects worry
about the perceptions of others or are concerned about reciprocation in later periods, the veil of anonymity
should allow them to behave more strategically. Finally we compare the original game to modified versions of the
game with two simplifications: using only three bidders, and not allowing for the period to repeat if all subjects
overbid in a given period. These simplifications make the game easier for the bidders to analyze.

Our first result is that subjects in the laboratory play the game very similarly to contestants on television.
This confirms that our laboratory environment is a reasonable proxy for real-world, high-stakes competitive
environments. (This is a common finding - laboratory studies using student subjects often generate behavior very
similar to the behavior of professionals in high-stakes environments, including stock traders and professional
auction bidders.) Second, we find that subjects perform slightly better as the game progresses, indicating some
degree of learning. Third, subjects are more strategic in anonymous treatments. For example, fourth bidders will
cutoff earlier bidders much more frequently under anonymity, but when decisions are publicly observable they tend
to leave a gap between their bid and the next-lowest bid. This indicates that subjects are fair-minded or worried
about future reciprocation. By having subjects rotate roles - alternating which gets to be the fourth bidder - we
can test whether reciprocation is an issue. We do not find any evidence that subjects target each other for
reciprocation; however, we do find that some groups use cutoff strategies frequently while other groups almost
never use them. Thus, some groups develop a "norm" of cut-throat behavior while other groups develop a norm of
cooperation. Finally, we find that when the game is simplified (by having fewer bidders or no resale rounds),
players play much closer to the game-theoretic predictions. Thus, the original Price Is Right is too complex for
players to solve, so they apparently use simple heuristics to guide their play. When the game is simplified, the
optimal strategies become apparent.

Ultimately, by understanding the basic science behind people's behavior in simple games such as these, we can
begin to describe how competitors should behave in other, more important real-world settings, such as R&D battles,
labor negotiations, firm location decisions, repeated auctions, or long-term competition between two rival firms.